diff --git a/python/paddle/fluid/layers/control_flow.py b/python/paddle/fluid/layers/control_flow.py
index feac42d94e62c171f5eb46fca01197884114c4eb..5354582aaa73c171d0f989e7ac321e51afe8dc30 100644
--- a/python/paddle/fluid/layers/control_flow.py
+++ b/python/paddle/fluid/layers/control_flow.py
@@ -76,13 +76,13 @@ def split_lod_tensor(input, mask, level=0):
     Examples:
         .. code-block:: python
 
-          x = layers.data(name='x', shape=[1])
+          x = fluid.layers.data(name='x', shape=[1])
           x.persistable = True
 
-          y = layers.data(name='y', shape=[1])
+          y = fluid.layers.data(name='y', shape=[1])
           y.persistable = True
 
-          out_true, out_false = layers.split_lod_tensor(
+          out_true, out_false = fluid.layers.split_lod_tensor(
                 input=x, mask=y, level=level)
 
     """
@@ -891,7 +891,7 @@ def array_write(x, i, array=None):
 
 def create_array(dtype):
     """
-    **Create LoDTensor Array**
+    **Create LoDTensorArray**
 
     This function creates an array of LOD_TENSOR_ARRAY . It is mainly used to
     implement RNN with array_write, array_read and While.
@@ -989,7 +989,8 @@ def array_read(array, i):
     Returns:
         Variable: The tensor type variable that has the data written to it.
     Examples:
-        .. code-block::python
+        .. code-block:: python
+
           tmp = fluid.layers.zeros(shape=[10], dtype='int32')
           i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
           arr = layers.array_read(tmp, i=i)
@@ -1027,7 +1028,7 @@ def shrink_memory(x, i, table):
 
 def array_length(array):
     """
-    **Get the length of Input LoDTensorArray**
+    **Get the Length of Input LoDTensorArray**
 
     This function performs the operation to find the length of the input
     LOD_TENSOR_ARRAY.
@@ -1042,12 +1043,13 @@ def array_length(array):
         Variable: The length of the input LoDTensorArray.
 
     Examples:
-        .. code-block::python
+        .. code-block:: python
 
           tmp = fluid.layers.zeros(shape=[10], dtype='int32')
           i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
           arr = fluid.layers.array_write(tmp, i=i)
           arr_len = fluid.layers.array_length(arr)
+
     """
     helper = LayerHelper('array_length', **locals())
     tmp = helper.create_tmp_variable(dtype='int64')
diff --git a/python/paddle/fluid/layers/learning_rate_scheduler.py b/python/paddle/fluid/layers/learning_rate_scheduler.py
index 2e5cff74c1dc1ddb4346c0c5bc6417f5293db14a..2dbc51c23fe940c3c42629eae27323712244c552 100644
--- a/python/paddle/fluid/layers/learning_rate_scheduler.py
+++ b/python/paddle/fluid/layers/learning_rate_scheduler.py
@@ -163,11 +163,11 @@ def polynomial_decay(learning_rate,
                      power=1.0,
                      cycle=False):
     """
-    **polynomial_decay**
+    **Polynomial Decay**
 
     Applies polynomial decay to the initial learning rate.
 
-    .. code-block::python
+    .. code-block:: python
 
      if cycle:
        decay_steps = decay_steps * ceil(global_step / decay_steps)
@@ -180,9 +180,9 @@ def polynomial_decay(learning_rate,
         learning_rate(Variable|float32): A scalar float32 value or a Variable. This
           will be the initial learning rate during training
         decay_steps(int32): A Python `int32` number.
-        end_learning_rate(float): A Python `float` number.
-        power(float): A Python `float` number
-        cycle(bool, Default False): Boolean. If set true, decay the learning rate every decay_steps.
+        end_learning_rate(float, Default: 0.0001): A Python `float` number.
+        power(float, Default: 1.0): A Python `float` number
+        cycle(bool, Default: False): Boolean. If set true, decay the learning rate every decay_steps.
 
     Returns:
         The decayed learning rate
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 3f3b7e20efe57c1e4ce07a24afe1ca312cf84577..7c4393c4d94787274314dfc249e6148f271103e8 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -1615,7 +1615,9 @@ def batch_norm(input,
 
     Can be used as a normalizer function for conv2d and fully_connected operations.
     The required data format for this layer is one of the following:
+
     1. NHWC `[batch, in_height, in_width, in_channels]`
+
     2. NCHW `[batch, in_channels, in_height, in_width]`
 
     Refer to `Batch Normalization: Accelerating Deep Network Training by Reducing